2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

Imaging Without Borders

2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

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Paper Detail

Paper IDARS-1.1
Paper Title USING FEATURE ALIGNMENT CAN IMPROVE CLEAN AVERAGE PRECISION AND ADVERSARIAL ROBUSTNESS IN OBJECT DETECTION
Authors Weipeng Xu, Hongcheng Huang, Shanghai Jiao Tong University, China; Shaoyou Pan, Shanghai Forensic Service Platform, Academy of Forensic Science, China
SessionARS-1: Object Detection
LocationArea I
Session Time:Tuesday, 21 September, 15:30 - 17:00
Presentation Time:Tuesday, 21 September, 15:30 - 17:00
Presentation Poster
Topic Image and Video Analysis, Synthesis, and Retrieval: Image & Video Interpretation and Understanding
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract The 2D object detection in clean images has been a well studied topic, but its vulnerability against adversarial attack is still worrying. Existing work has improved robustness of object detectors by adversarial training, at the same time, the average precision on clean images drops significantly. In this paper, we propose that using feature alignment of intermediate layer can improve clean average precision and robustness in object detection. Further, on the basis of adversarial training, we present two feature alignment modules: Knowledge-Distilled Feature Alignment module and Self-Supervised Feature Alignment module, which can guide the network to generate more effective features. We conduct extensive experiments on PASCAL VOC and MS-COCO datasets to verify the effectiveness of our proposed approach. The code of our experiments is available at https://github.com/grispeut/Feature-Alignment.git.